ArticleFrontiers in oncology2022
Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research progress of MRI-based radiomics in hepatocellular carcinoma.Frontiers in oncology · 2025Pooled it
- Ethnic diversity in treatment response for colorectal cancer: proof of concept for radiomics-driven enrichment trials.European radiology · 2023Trial
- AI-Driven Radiomics Assisted Prognostic Modeling for Hepatocellular Carcinoma with Portal Vein Invasion: A Retrospective Study.Biomedicines · 2026Article
- Predictability of clinical outcomes after external beam radiotherapy for hepatocellular carcinoma according to tumor marker dynamics.PloS one · 2025Article
- Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study.Journal of hepatocellular carcinoma · 2025Article
- Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence.Journal of hepatocellular carcinoma · 2024Review
- Development and verification of a nomogram for predicting portal vein tumor thrombosis in hepatocellular carcinoma.American journal of translational research · 2024Article
- CT-based radiomics nomogram for prediction of survival after transarterial chemoembolization with drug-eluting beads in patients with hepatocellular carcinoma and portal vein tumor thrombus.European radiology · 2023Article
- Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.Frontiers in neuroscience · 2022Article
- Predicting Stereotactic Body Radiation Therapy Response Using an AI-Based Tumor Vessel Biomarker.Technology in cancer research & treatmentArticle
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8 authors.
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Abstract
Background: This study aims to establish and validate a predictive model based on radiomics features, clinical features, and radiation therapy (RT) dosimetric parameters for overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with RT for portal vein tumor thrombosis (PVTT). Methods: We retrospectively reviewed 131 patients. Patients were randomly divided into the training ( Results: Two radiomics features were extracted to calculate the rad-score for the prediction of OS. The radiomics-based nomogram had better performance than the clinical nomogram for the prediction of OS, with a C-index of 0.73 (95% CI, 0.67-0.79) and an AUC of 0.71 (95% CI, 0.62-0.79). The predictive accuracy was assessed by a calibration curve. Conclusion: The radiomics-based predictive model significantly improved OS prediction in HCC patients treated with RT for PVTT.
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